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

• URL: http://arxiv.org/abs/2604.16010v1
• Date: Fri, 17 Apr 2026 12:33:45 GMT
• Title: IA-CLAHE: Image-Adaptive Clip Limit Estimation for CLAHE
• Abstract summary: Image-adaptive contrast limited adaptive histogram equalization (IA- CLAHE)<n> Conventional CLAHE is widely used to boost the performance of various computer vision tasks.<n>IA- CLAHE addresses this limitation by adaptively estimating tile-wise clip limits from the input image.
• Score: 2.5282283486446753
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Fine-grained CLIP fine-tuning with self-annotated region alignment [57.28808560528696]
  Contrastive Language-Image Pre-training has been shown to have limitations in its fine-grained dense feature representation.<n>We propose SFF-CLIP (Self-annotated Fine-grained Fine-tuning for CLIP) to boost the fine-grained representation ability in the CLIP fine-tuning.
  arXiv  Detail & Related papers  (2026-07-15T10:06:39Z)
• Self-Calibrated CLIP for Training-Free Open-Vocabulary Segmentation [19.749490092520006]
  Self-Calibrated CLIP (SC-CLIP) is a training-free method that calibrates CLIP to produce finer representations.<n>SC-CLIP boosts the performance of vanilla CLIP ViT-L/14 by 6.8 times.
  arXiv  Detail & Related papers  (2024-11-24T15:14:05Z)
• Dual-Modal Prompting for Sketch-Based Image Retrieval [76.12076969949062]
  We propose a dual-modal CLIP (DP-CLIP) network, in which an adaptive prompting strategy is designed. We employ a set of images within the target category and the textual category label to respectively construct a set of category-adaptive prompt tokens and channel scales. Our DP-CLIP outperforms the state-of-the-art fine-grained zero-shot method by 7.3% in Acc.@1 on the Sketchy dataset.
  arXiv  Detail & Related papers  (2024-04-29T13:43:49Z)
• Make Continual Learning Stronger via C-Flat [12.569738684003923]
  We propose a Continual Flatness (C-Flat) method featuring a flatter loss landscape tailored for Continual Learning (CL) C-Flat could be easily called with only one line of code and is plug-and-play to any CL methods.
  arXiv  Detail & Related papers  (2024-04-01T08:18:38Z)
• CLIP Brings Better Features to Visual Aesthetics Learners [14.351572852317558]
  Image Aesthetics Assessment (IAA) is a challenging task due to its subjective nature and expensive manual annotations.<n>Recent large-scale vision-language models, such as Contrastive Language-Image Pre-training (CLIP), have shown their promising representation capability for various downstream tasks.<n>We propose a two-phase CLIP-based Semi-supervised Knowledge Distillation paradigm, aiming to learn a lightweight IAA model while leveraging CLIP's strong generalization capability.
  arXiv  Detail & Related papers  (2023-07-28T16:00:21Z)
• Condition-Invariant Semantic Segmentation [77.10045325743644]
  We implement Condition-Invariant Semantic (CISS) on the current state-of-the-art domain adaptation architecture.<n>Our method achieves the second-best performance on the normal-to-adverse Cityscapes$to$ACDC benchmark.<n>CISS is shown to generalize well to domains unseen during training, such as BDD100K-night and ACDC-night.
  arXiv  Detail & Related papers  (2023-05-27T03:05:07Z)
• CLIPood: Generalizing CLIP to Out-of-Distributions [73.86353105017076]
  Contrastive language-image pre-training (CLIP) models have shown impressive zero-shot ability, but the further adaptation of CLIP on downstream tasks undesirably degrades OOD performances. We propose CLIPood, a fine-tuning method that can adapt CLIP models to OOD situations where both domain shifts and open classes may occur on unseen test data. Experiments on diverse datasets with different OOD scenarios show that CLIPood consistently outperforms existing generalization techniques.
  arXiv  Detail & Related papers  (2023-02-02T04:27:54Z)
• No Token Left Behind: Explainability-Aided Image Classification and Generation [79.4957965474334]
  We present a novel explainability-based approach, which adds a loss term to ensure that CLIP focuses on all relevant semantic parts of the input. Our method yields an improvement in the recognition rate, without additional training or fine-tuning.
  arXiv  Detail & Related papers  (2022-04-11T07:16:39Z)
• Adaptive Cross-Layer Attention for Image Restoration [10.795382525098917]
  Non-local attention processes features of each layer separately, so it risks missing correlation between features among different layers. We propose a novel Adaptive Cross-Layer Attention (ACLA) module. Two adaptive designs are proposed for ACLA: (1) adaptively selecting the keys for non-local attention at each layer; (2) automatically searching for the insertion locations for ACLA modules.
  arXiv  Detail & Related papers  (2022-03-04T22:16:18Z)
• Orthogonal Projection Loss [59.61277381836491]
  We develop a novel loss function termed Orthogonal Projection Loss' (OPL) OPL directly enforces inter-class separation alongside intra-class clustering in the feature space. OPL offers unique advantages as it does not require careful negative mining and is not sensitive to the batch size.
  arXiv  Detail & Related papers  (2021-03-25T17:58:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.