# De-Sharpen GoPro/DJI Halos: 3 Criteria for VSR PSNR

Marcus Vance · August 23, 2026

> De-Sharpen GoPro/DJI Halos: 3 Criteria for VSR PSNR. Sharper looks better—until it doesn't. Unsharp masking, the engine behind in-c...

| Takeaway | Detail |
| --- | --- |
| Halos are overshoot artifacts, not recovered detail. | Unsharp masking sharpens by exaggerating brightness differences along edges; applied aggressively it creates visible under- and overshoots called sharpening halos, and it cannot reconstruct the ideal edge profile—only the appearance of more pronounced acutance (Cambridge in Colour). |
| Audit suspect footage at 200% zoom before feeding it to a VSR pipeline. | Cambridge in Colour demonstrates halo formation with soft, mild-sharpened, and over-sharpened comparisons rendered at 200% zoom for visibility. |
| Calibrated de-sharpening ranges already ship inside camera menus. | Nikon's Quick sharp Picture Control spans -2 ('Softer') to +2 ('Sharper'), with -2 mapping to Sharpening -0.5, Mid-range sharpening -1, and Clarity -1, versus Standard defaults of Sharpening 3, Mid-range sharpening 2, Clarity 1 (Nikon Z7/Z6 Tips). |
| Sharpening kernels mathematically amplify the edge signals a super-resolution network inherits. | Laplacian sharpening convolves image A with the mask [1 1 1; 1 -8 1; 1 1 1] and outputs C = A - B, highlighting gray-level discontinuities while deemphasizing slowly varying regions (Anil Ozen, Medium). |

Sharper looks better—until it doesn't. Unsharp masking, the engine behind in-camera sharpening, works by exaggerating brightness differences along edges; pushed too hard, it manufactures under- and overshoots called sharpening halos (Cambridge in Colour). And sharpening cannot reconstruct an ideal edge profile—it only fakes pronounced acutance. Hand those haloed frames to a video super-resolution model and you're asking it to upscale fiction.

The fix already sits in camera menus. Nikon's Quick sharp Picture Control spans -2 ('Softer') to +2 ('Sharper'); set to -2, it rebalances Sharpening to -0.5, Mid-range sharpening to -1, and Clarity to -1 (Nikon Z7/Z6 Tips). Manufacturers ship calibrated de-sharpening ranges, so pull the equivalent picture-profile sliders toward soft on a GoPro or DJI body before any 2026 VSR run.

Why halos hurt PSNR is arithmetic. A Laplacian pass convolves the image with a mask whose center weight is -8 and outputs C = A - B, amplifying gray-level discontinuities while deemphasizing smooth regions (Anil Ozen, Medium)—precisely the signal a super-resolution network inherits. Audit suspects at 200% zoom, where Cambridge in Colour's soft-versus-over-sharpened comparisons make halos unmistakable, and let the model—not the firmware—manufacture the crispness.

![Aerial drone view winding coastal cliff highway golden](https://static.mm-ais.com/article-images-ai/de-sharpen-gopro-dji-halos-3-criteria-fo-ai-f55ed1ce.jpg)
Aerial drone view winding coastal cliff highway golden

## How It Works

Nine numbers explain the entire halo problem. Laplacian sharpening computes the output C as A minus B, where B is the image A convolved with a 3×3 mask carrying −8 at its center and 1 in every other position — according to Anil Ozen's published walkthrough on Medium. That is the complete operation: a fixed linear filter pushing each pixel away from its neighborhood average. Nothing in it adds scene detail, which is exactly why the result can be undone.

Two independent references converge on the same reading. According to Cambridge in Colour's guide to image sharpening, most sharpening software applies an "unsharp mask" which, despite its name, sharpens; the craft lies in balancing sufficiently pronounced edges against minimizing the visible under- and overshoots called sharpening halos. The MATLAB-based sharpening literature frames it identically: enhancement of edge appearance over existing content, never reconstruction of lost detail. Mechanically, a baked-in halo is removable artifact, not signal — the premise this entire guide rests on.

Why this lands directly on VSR PSNR: a super-resolution network treats strong edge-adjacent gradients as candidates for reconstruction. Halos corrupt that decision twice. Spatially, they contribute squared error at every edge whether the network reproduces them or burns capacity fighting them. Temporally, their amplitude wobbles frame to frame with compression noise — precisely the flicker that temporal-consistency objectives penalize. Strip the halo before inference and the network spends its budget on texture instead of artifact suppression.

The cheapest correct intervention point is before capture. Nikon's official Z7/Z6 tips documentation describes its Quick sharp Picture Control as "a quick way of making pictures sharper or softer," spanning −2 ("Softer") to +2 ("Sharper") — documented proof that this parameter class ships in-camera, applied before the file ever reaches post. GoPro and DJI expose analogous strength sliders, but treat any online preset with suspicion: the indexed technical literature contains zero GoPro- or DJI-specific entries — no in-camera sharpening levels, no measured halo amplitudes, no firmware-named parameters — so profile your own unit with a slanted-edge chart rather than trusting secondhand values.

One boundary condition decides whether correction is even possible: clipping. Once an overshoot pins at the maximum code value, the true level is gone and no inverse filter reconstructs it — prevention strictly dominates post-hoc repair. This also kills the tired claim that the conventional approach wastes money on unnecessary steps: setting sharpening to minimum and running one guided-filter pass costs nothing, while the genuinely expensive route is paying premium restoration compute to suppress halos you chose to bake in. Even the tooling is unencumbered — Google Patents lists the relevant image-sharpening-processing method as "Expired – Fee Related," with adjusted expiration 2029-12-12, so implementing the inverse carries no licensing exposure.

| Term | Mechanical definition | Consequence for VSR PSNR |
| --- | --- | --- |
| Laplacian sharpening | C = A − B; B = A convolved with mask [1 1 1; 1 −8 1; 1 1 1] (Anil Ozen, Medium) | Fixed linear filter, therefore invertible wherever values stay unclipped |
| Unsharp mask | Adds a scaled difference between the image and a blurred copy; despite the name, it sharpens (Cambridge in Colour) | Injects synthetic high-frequency energy the network must reproduce or suppress |
| Overshoot / undershoot | Brightness pushed above / below true edge levels on opposite sides of a transition | Direct per-pixel error against ground truth at every edge |
| Sharpening halo | The visible under-/overshoot pair left when edge gain outruns balance (Cambridge in Colour) | The artifact this guide targets for removal |
| In-camera sharpening parameter | Nikon Quick sharp Picture Control: "a quick way of making pictures sharper or softer," −2 (Softer) to +2 (Sharper) (Nikon Z7/Z6 TIPS) | Applied pre-capture; minimizing it leaves nothing to undo downstream |
| Clipping | Overshoot pinned at the maximum code value | Irreversible — hard ceiling on any post-hoc PSNR recovery |
| VSR PSNR | Log-scale fidelity of the super-resolved frame against ground truth | Halo energy concentrates squared error along edges, capping attainable scores |

![First person mountain bike trail rushing through misty alpine](https://static.mm-ais.com/article-images-ai/de-sharpen-gopro-dji-halos-3-criteria-fo-ai-2d431ec4.jpg)
First person mountain bike trail rushing through misty alpine

## Key Factors to Consider

The three criteria that determine whether a de-sharpen pass actually lifts your VSR PSNR are all verifiable from documentation — no benchmark suite required. They are: where the sharpening entered the file, which color channel it modified, and whether your upscaler quietly re-applies it on the way out. Ranked by leverage, provenance dominates, because it decides whether the other two corrections are even necessary.

**Criterion one: provenance.** According to Nikon's Z7/Z6 TIPS documentation, 'NEF (RAW) Processing' is listed as the alternate route for adjusting sharpening or softening after capture — the manufacturer explicitly treating sharpening as a deferrable, reversible decision. GoPro and DJI offer no equivalent route: their sharpening is fused into firmware before the file reaches your storage card. That asymmetry is the entire problem. If the decision cannot be deferred upstream, it must be inverted downstream — estimate the overshoot and subtract it before the super-resolution network ever sees the frame.

**Criterion two: the channel.** According to patent US8351733B2, sharpening hardware performs the operation "by changing only luminance without changing a hue." Halo ringing lives in precisely that channel. The practical consequence: a chroma-focused cleanup pass will never touch your halos, while a luminance-only correction will. One edge case refines this further — because the Laplace operator highlights gray-level discontinuities and deemphasizes slowly varying regions, as Anil Ozen summarizes, the damage concentrates at high-contrast boundaries: a drone's horizon line against sky, a GoPro's roofline against overcast. Genuinely flat interiors escape mostly unharmed, so scan boundaries first when calibrating your inverse pass.

**Criterion three: re-contamination downstream.** According to Upsampler's product page, its 'Precise Upscale' tool enlarges images "while staying true to the original, sharpening, deblurring, and reducing noise," and the company markets video upscaling "up to 4K" using state-of-the-art video super-resolution. Read that feature list carefully: sharpening is bundled into the very pipeline you are feeding. A flawless de-sharpen pass is wasted if the upscaler's enhancement stage reintroduces overshoot on output. Audit every stage between your correction and final delivery.

**The numbers that matter** reduce to one: the PSNR delta between your pipeline with and without the de-sharpen stage. A source audit of the currently available vendor materials found no PSNR, SSIM, or LPIPS value published for any VSR model — resolution ceilings like "up to 4K" are capacity claims, not fidelity measurements. Verify locally: same clip, two runs, one metric, computed against ground truth you control. And discard the comfortable assumption that factory sharpening defaults arrive pre-tuned for machine vision. According to Nikon's Z7/Z6 Tips, a single Quick sharp adjustment is automatically matched by balanced adjustments to Sharpening, Mid-range sharpening, and Clarity — the "one dial" is three coupled parameters, so there is no single factory value any network was calibrated against. As Cambridge in Colour documents, pushed too aggressively, sharpening produces unsightly artifacts: exactly the overshoot your VSR model is then forced to hallucinate around.

| Decision criterion | Named evidence | Winning choice | Why it wins |
| --- | --- | --- | --- |
| Sharpening provenance | Nikon Z7/Z6 TIPS: NEF (RAW) Processing adjusts sharpening after capture | Defer or omit upstream | Firmware-baked sharpening (GoPro/DJI) must instead be inverted downstream |
| Color channel corrected | US8351733B2: "changing only luminance without changing a hue" | Luminance-only inverse pass | Halos are luminance ringing; chroma tools bypass them entirely |
| Strength adjustment method | Nikon Z7/Z6 Tips: Quick sharp couples Sharpening, Mid-range, Clarity | Coupled sliders, conservative settings | Aggressive single-dial pushes create artifacts (Cambridge in Colour) |
| Pipeline stages after correction | Upsampler Precise Upscale bundles "sharpening, deblurring, and reducing noise" | Disable or bypass bundled sharpening | Output-side re-sharpening erases the de-sharpen gain |
| Quality evidence accepted | Source audit: no PSNR/SSIM/LPIPS published by any fetched VSR vendor | Self-run A/B PSNR on matched clips | "Up to 4K" is a capacity claim, not a fidelity measurement |

![Key Factors to Consider — De-Sharpen GoPro/DJI Halos](https://static.mm-ais.com/article-images-pixabay/de-sharpen-gopro-dji-halos-3-criteria-fo-1ae3edf9.jpg)

## Common Mistakes

Imgupscaler.ai bills one credit per job and accepts a maximum of three images per upload — constraints worth knowing because the most common de-sharpening failure runs in the wrong direction entirely: pushing haloed GoPro frames through an AI sharpener to "clean them up." According to the Guide to Image Sharpening, the operation "isn't able to reconstruct the ideal image"; it manufactures "the appearance of a more pronounced edge." Every credit spent there deepens the very overshoot your super-resolution model then has to fight.

Pitfall 1, stated plainly: adding acutance upstream of VSR and expecting detail to materialize. Unsharp masking works by exaggerating brightness differences along edges, as Cambridge in Colour describes — which is precisely the distortion a network trained on consistent degradations reads as corruption rather than content. The stubborn myth is that sharper inputs give the model "more to work with." They don't; added sharpness is perceived edge contrast, not restored resolution, and the acutance-versus-resolution distinction in the canonical sharpening guide settles the question. Picture the concrete version: a creator exports stills from a Hero clip, batches them three at a time through the browser sharpener, and repeats across hundreds of frames — paying per job while handing the model progressively worse-conditioned inputs.

Pitfall 2 is subtler: single-knob de-sharpening. Consumer firmware almost never exposes one sharpening operator. Nikon's Z7/Z6 tips documentation gives the cleanest public decomposition — Quick Sharp set to −2 ("Softer") maps to Sharpening −0.5, Mid-range sharpening −1, and Clarity −1 simultaneously. Three stacked local-contrast channels behind one menu item. Action-cam pipelines layer controls the same way, though the split varies by manufacturer and firmware generation, so verify per model. Flip only the slider labeled "Sharpness" and whatever mid-range or clarity-style operator survives keeps injecting overshoot. Work on graph-based edge-preserving smoothing explains why: negative coefficients inside such filters produce edge-enhancing behavior — corner and edge overshoot, the mathematical signature that renders as visible halos along high-contrast boundaries. Any surviving operator with negative lobes regenerates the artifact.

| Mistake | Concrete instance | Documented mechanism | Correction |
| --- | --- | --- | --- |
| Sharpening to "help" VSR | Haloed GoPro stills run through Imgupscaler.ai — 1 credit per job, 3-image upload cap | Creates the appearance of a more pronounced edge; cannot reconstruct the ideal image (Guide to Image Sharpening) | De-sharpen first; reserve credits for post-VSR finishing only |
| One-knob de-sharpen | Dropping only the "Sharpness" control to minimum | Firmware stacks separate channels: Nikon Quick Sharp −2 = Sharpening −0.5 + Mid-range −1 + Clarity −1 | Audit mid-range and clarity channels; confirm on a test clip |
| Trusting menu labels | Assuming "Low" sharpness means no processing occurred | Sensors and lenses always blur, so every digital image receives sharpening correction, knowingly or not (Cambridge in Colour) | Verify pixels, not presets — scan boundary fringes at full zoom |
| Dismissing halos as cosmetic | Ignoring fringes because playback "looks fine" on a phone | Landsat 8 pan-sharpening debates cite "high or some (tolerable) loss of spectral quality" in downstream indices (ResearchGate) | Treat overshoot as measurable error; gate batches on the fringe test |

The verification habit that catches both pitfalls costs nothing: record ten seconds of chain-link fence against sky, or a white hull on dark water, then step through frames at full zoom. A halo announces itself as a bright fringe on the dark side of a boundary and a dark fringe on the light side. If fringes survive your one-slider fix, you have located a second operator — go find its control rather than re-running the first.

Skeptics who wave off halos as cosmetic should look at remote sensing. According to practitioner discussions on ResearchGate, teams working with Landsat 8 openly debate whether pan-sharpening reflective bands causes "high or some (tolerable) loss of spectral quality" in composites and the indices computed from them. A measurement-driven field treats sharpening as quantifiable fidelity loss; PSNR deserves identical standing.

So the 2026 checklist is short: never sharpen before VSR, audit every local-contrast channel instead of one labeled slider, and gate each batch on the fringe test. Get those right and whatever mistakes remain in your pipeline will at least be novel ones.

![Common Mistakes — De-Sharpen GoPro/DJI Halos](https://static.mm-ais.com/article-images-pixabay/de-sharpen-gopro-dji-halos-3-criteria-fo-92a8fbde.jpg)

## Insider Tactics

Stop trying to invert the sharpening kernel — the edge you're chasing was never recorded. According to Cambridge in Colour, sharpening cannot reconstruct the ideal edge profile; it only manufactures the appearance of a more pronounced edge, what photographers call acutance. The MATLAB sharpening literature formalizes the same point: the operation exists to "enhance and extract the edge and contour of the scene in the image," amplifying gradients already present and adding zero scene information. Every pixel inside a GoPro/DJI halo lobe is therefore pure error signal against a clean reference — suppress it and you lose nothing recoverable.

The non-obvious strategy: treat the camera's sharpening as a bounded forward model, not an unknown degradation. Cambridge in Colour documents that sharpening controls are reasonably standardized across editing software, typically offering at least three adjustable parameters — so skip blind deconvolution entirely. Estimate the knobs straight off the halo: lobe width pins the radius, lobe height pins the amount, the noise response pins the threshold. Rebuild the overshoot synthetically and subtract it. You aren't restoring the edge; you're canceling a synthetic artifact with a synthetic replica of itself — a well-posed problem, unlike inversion. The belief that undoing sharpening demands heavyweight deconvolution machinery dies right here.

Why the halos exist at all: the firmware optimized a different objective than yours. A published mammography pipeline — Detection of Microcalcifications in Mammography using Image Processing, Figure 6 — applies a dedicated sharpening step exactly where edge visibility, not pixel-level fidelity, is the goal. Action-cam engineers inherited that detection-first logic; the halo makes power lines and horizon edges pop on a phone screen. Your VSR network scores PSNR against clean ground truth, so the very feature the firmware added is the feature your loss function penalizes hardest. You're stripping a display aid, not repairing a defect.

The timing tip runs on two clocks. Pipeline order first: de-sharpen before any temporal stage. Each interpolated frame inherits the overshoot pattern from both of its source frames, so a halo that survives into interpolation gets stamped onto synthesized frames too — clean afterward and you're scrubbing contamination that has already propagated. Then the historical clock: according to Google Patents, the sharpening-method lineage behind these firmware kernels carries a prior-art date of 2008-02-04, an application filed 2008-12-24, and a grant issued 2013-01-08. That lifecycle predates modern VSR training corpora, which is why archival GoPro/DJI clips ship with undocumented settings — estimate the three parameters per clip, never from a spec sheet that no longer exists.

One retuning trigger most operators miss: according to Nikon's Z7/Z6 tips, recommended sharpening settings vary with output size, and removal settings inherit the same dependence. A pass calibrated at a lower delivery resolution over-smooths once the master goes to 4K, because lobe geometry scales with the resampling chain — retune at every delivery-resolution change. Tool choice matters less than assumed: the three-knob scheme is standardized across the full span of Worldmetrics' makeover-software ranking, published and verified July 20, from Adobe Photoshop down to RawTherapee, so fitted parameters transfer between packages. Next action: take one halo-heavy clip, measure lobe width and height at a high-contrast edge, solve the three parameters, subtract, and score both versions through your VSR validation split — the delta is the headroom your firmware was spending on acutance theater.

| Tactic | Anchor (source, figure) | When it wins |
| --- | --- | --- |
| Blind inversion of the sharpen kernel | Cambridge in Colour: ideal edge profile unrecoverable | Never — the data was never captured |
| Three-parameter refit, subtract rebuilt overshoot | Cambridge in Colour: typically at least three standardized parameters | Default primary — bounded and per-clip solvable |
| Edge-gated smoothing on overshoot lobes only | Paired opposing filter families (Filters for Noise Reduction and Edge Detection) | Fallback when lobe geometry is ambiguous |
| De-sharpen before temporal stages | Interpolated frames inherit overshoot from both source frames | Always — order is non-negotiable |
| Per-clip estimation for archival footage | Google Patents: prior art 2008-02-04; granted 2013-01-08 | Mandatory for pre-deep-learning libraries |
| Retune on output-size change | Nikon Z7/Z6 tips: sharpening varies with output size | Every delivery-resolution switch |

![Insider Tactics — De-Sharpen GoPro/DJI Halos](https://static.mm-ais.com/article-images-pixabay/de-sharpen-gopro-dji-halos-3-criteria-fo-de69454b.jpg)

## Comparison

Cambridge in Colour's halo demonstration is the comparison baseline worth memorizing: the same edge rendered three ways — soft, mildly sharpened, over-sharpened — at 200% zoom, so the overshoot ring finally becomes large enough to classify by eye. Before any tool debate, classify which of those three states your GoPro or DJI clip actually occupies, because the winning preprocessing option flips depending on the answer. And the reason manufacturers over-sharpen in the first place is not carelessness: according to Cambridge in Colour, correctly applied sharpening can improve apparent image quality even more than upgrading to a high-end camera lens. The halo is the cost of a feature that works.

What you are undoing depends on which filter family baked the edge in. According to Maxence Boels' sharpening survey on Medium, the documented families number three: the Laplacian (second derivative), the Gradient (first-order derivative), and Sobel operators. They fail differently, which is exactly what a fair comparison has to account for. Per the Image Processing-Sharpening Spatial Filters walkthrough, also on Medium, the Laplace operator "highlights gray-level discontinuities in an image and deemphasizes regions with slowly varying gray levels" — so a Laplacian-born halo concentrates at edges and vanishes across flat water or sky, while first-order responses track slope and behave differently along diagonals. If you cannot identify the family, treat the halo as Laplacian-class first; it is the classic spatial sharpening kernel and the most common factory choice.

Whichever option you pick, the side-by-side is only valid if both arms share one sharpening state. RawTherapee supports batch consistency through reusable processing parameters, enabling dataset-level comparison of exposure, white balance stability, noise, and sharpening outcomes — and running an entire multi-file capture through one identical profile before any evaluation is the established normalization pattern. Apply it here: normalize the full GoPro-plus-DJI card dump through a single profile, then split into arms. An A/B run on mismatched states measures your pipeline's inconsistency, not the de-sharpen gain.

Now kill the assumption that pre-sharpened input hands the super-resolution stage more usable detail. Upsampler markets its VSR as reconstructing "sharp edges, fine textures, and readable text on every frame, keeping motion stable" — the model manufactures edge acutance itself, so feeding it factory-ringed edges stacks one enhancement on top of another. Be equally skeptical of any guide quoting exact PSNR deltas for de-sharpening: no current vendor documentation defines a de-sharpening kernel, halo-suppression threshold, or blur radius, and IJISRT's MATLAB sharpening treatment publishes no extractable figures either. The honest comparison numbers available today are the structural ones below; the dB delta is something you measure on your own normalized set.

| Scenario | Winning option | Verified anchor |  |
| --- | --- | --- | --- |
| Halo ring visible on edges (matches the over-sharpened panel) | Batch-normalize through one shared profile, then run VSR | Cambridge in Colour over-sharpened panel; RawTherapee reusable parameter sets |  |
| Clip matches the soft panel — no ring anywhere | Skip de-sharpening; send straight to VSR | Cambridge in Colour: correct sharpening can beat a lens upgrade |  |
| Mixed GoPro + DJI + mirrorless session | One identical profile across every file before splitting arms | RawTherapee batch-consistency workflow |  |
| Mirrorless B-cam on untouched Picture Controls | Account for the factory state: Sharpening 3, Mid-range sharpening 2, Clarity 1 | Nikon Z7/Z6 tips documentation |  |
| Filter family unknown | Assume Laplacian-class first | Boels (Medium); L ``` Frequently Asked Questions If I set Nikon's Quick sharp Picture Control to -2, what exact parameters change inside the camera? Setting Quick sharp to -2 ('Softer') maps to Sharpening -0.5, Mid-range sharpening -1, and Clarity -1, versus the Standard defaults of Sharpening 3, Mid-range sharpening 2, and Clarity 1. At what magnification should I inspect footage for halos before feeding it to a super-resolution model? Audit suspect footage at 200% zoom, the magnification Cambridge in Colour uses for its soft-, mild-sharpened, and over-sharpened comparisons that make halos unmistakable. Can a de-sharpen pass recover an edge whose overshoot already pinned at the top of the signal range? No — once an overshoot pins at the maximum code value the true level is gone and no inverse filter reconstructs it, which is why prevention strictly dominates post-hoc repair. Do sharpening halos live in the color data, or could a chroma-focused cleanup pass remove them? According to patent US8351733B2, sharpening hardware operates "by changing only luminance without changing a hue," so halos sit entirely in the luminance channel and a chroma-focused cleanup pass will never touch them. Will Upsampler's Precise Upscale quietly re-add sharpening after I clean my footage? Yes — Upsampler describes Precise Upscale as enlarging images "while staying true to the original, sharpening, deblurring, and reducing noise" while marketing video upscaling up to 4K, so sharpening is bundled into the very pipeline you are feeding. Are there published sharpening levels or measured halo amplitudes I can trust for my specific GoPro or DJI body? No — the indexed technical literature contains zero GoPro- or DJI-specific entries (no in-camera sharpening levels, no measured halo amplitudes, no firmware-named parameters), so profile your own unit with a slanted-edge chart rather than trusting secondhand values. Quick answers What are sharpening halos? | They are visible under- and overshoots created when unsharp masking exaggerates brightness differences along edges too aggressively — overshoot artifacts, not recovered detail. |
| At what zoom level should suspect footage be audited before a VSR pipeline? | At 200% zoom, where Cambridge in Colour's soft-versus-over-sharpened comparisons make halos unmistakable. |  |  |
| What range does Nikon's Quick sharp Picture Control span, and what does -2 map to? | It spans -2 ('Softer') to +2 ('Sharper'), with -2 mapping to Sharpening -0.5, Mid-range sharpening -1, and Clarity -1. |  |  |
| What is the Laplacian sharpening operation described in the article? | C = A − B, where B is the image A convolved with a 3×3 mask carrying −8 at its center and 1 in every other position. |  |  |
| What boundary condition decides whether de-sharpening correction is even possible? | Clipping: once an overshoot pins at the maximum code value, the true level is gone and no inverse filter can reconstruct it, so prevention strictly dominates post-hoc repair. |  |  |

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