IA-CLAHE: Image-Adaptive Clip Limit Estimation for CLAHE

TL;DR

This paper introduces IA-CLAHE, an adaptive histogram equalization method that estimates clip limits based on input images, improving both recognition accuracy and visual quality without task-specific training.

Contribution

It proposes a novel end-to-end trainable clip limit estimator for CLAHE that generalizes across diverse conditions without needing ground-truth data.

Findings

Improves recognition performance across various tasks.

Enhances visual quality for human perception.

Does not require task-specific training data.

Abstract

This paper proposes image-adaptive contrast limited adaptive histogram equalization (IA-CLAHE). Conventional CLAHE is widely used to boost the performance of various computer vision tasks and to improve visual quality for human perception in practical industrial applications. CLAHE applies contrast limited histogram equalization to each local region to enhance local contrast. However, CLAHE often leads to over-enhancement, because the contrast-limiting parameter clip limit is fixed regardless of the histogram distribution of each local region. Our IA-CLAHE addresses this limitation by adaptively estimating tile-wise clip limits from the input image. To achieve this, we train a lightweight clip limits estimator with a differentiable extension of CLAHE, enabling end-to-end optimization. Unlike prior learning-based CLAHE methods, IA-CLAHE does not require pre-searched ground-truth clip limits or task-specific datasets, because it learns to map input image histograms toward a domain-invariant uniform distribution, enabling zero-shot generalization across diverse conditions. Experimental results show that IA-CLAHE consistently improves recognition performance, while simultaneously enhancing visual quality for human perception, without requiring any task-specific training data.