CVFeb 22, 2021

Shadow Image Enlargement Distortion Removal

arXiv:2102.11356v1
Originality Synthesis-oriented
AI Analysis

This work addresses image quality issues for applications requiring shadow image enlargement, but it is incremental as it builds on standard interpolation and filtering techniques.

The paper tackles the problem of distortion in enlarged shadow images by applying a three-step preprocessing pipeline (interpolation, average filter, unsharp filter) to reduce errors, achieving results that closely match the original enlarged image with minimal differences.

This project aims to adopt preprocessing operations to get less distortions for shadow image enlargement. The preprocessing operations consists of three main steps: first enlarge the original shadow image by using any kind of interpolation methods, second apply average filter to the enlargement image and finally apply the unsharp filter to the previous averaged image. These preprocessing operations leads to get an enlargement image very close to the original enlarge image for the same shadow image. Then comparisons established between the adopted image and original image by using different types of interpolation and different alfa values for unsharp filter to reach the best way which have less different errors between the two images.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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