Images acquired in hazy conditions have degradations induced in them.
Dehazing such images is a vexed and ill-posed problem. Scores of prior-based
and learning-based approaches have been proposed to mitigate the effect of haze
and generate haze-free images. Many conventional methods are constrained by
their lack of awareness regarding scene depth and their incapacity to capture
long-range dependencies. In this paper, a method that uses residual learning
and vision transformers in an attention module is proposed. It essentially
comprises two networks: In the first one, the network takes the ratio of a hazy
image and the approximated transmission matrix to estimate a residual map. The
second network takes this residual image as input and passes it through
convolution layers before superposing it on the generated feature maps. It is
then passed through global context and depth-aware transformer encoders to
obtain channel attention. The attention module then infers the spatial
attention map before generating the final haze-free image. Experimental
results, including several quantitative metrics, demonstrate the efficiency and
scalability of the suggested methodology.

提出了一种使用残差学习和视觉转换器的注意模块的方法，首先估计残差图像，然后通过卷积层和全局上下文转换器编码器获取通道注意力，最后生成无雾图像。实验结果证明了该方法的高效性和可扩展性。

DHFormer：基于视觉 Transformer 的图像去雾注意力模块

DHFormer: A Vision Transformer-Based Attention Module for Image Dehazing

Existing methods attempt to improve models' generalization ability on
real-world hazy images by exploring well-designed training schemes (e.g.,
cycleGAN, prior loss). However, most of them need very complicated training
procedures to achieve satisfactory results. In this work, we present a totally
novel testing pipeline called Prompt-based Test-Time Dehazing (PTTD) to help
generate visually pleasing results of real-captured hazy images during the
inference phase. We experimentally find that given a dehazing model trained on
synthetic data, by fine-tuning the statistics (i.e., mean and standard
deviation) of encoding features, PTTD is able to narrow the domain gap,
boosting the performance of real image dehazing. Accordingly, we first apply a
prompt generation module (PGM) to generate a visual prompt, which is the source
of appropriate statistical perturbations for mean and standard deviation. And
then, we employ the feature adaptation module (FAM) into the existing dehazing
models for adjusting the original statistics with the guidance of the generated
prompt. Note that, PTTD is model-agnostic and can be equipped with various
state-of-the-art dehazing models trained on synthetic hazy-clean pairs.
Extensive experimental results demonstrate that our PTTD is flexible meanwhile
achieves superior performance against state-of-the-art dehazing methods in
real-world scenarios.

测试环节具有在真实场景中生成逼真结果的全新方法 —— 基于提示的测试时去雾（PTTD）周期，该方法通过微调编码特征的统计值来缩小领域差距，提高真实图像去雾的性能，并且在灵活性方面取得了优越的性能表现。

基于提示的实时图像去雾测试流程：一种新的流水线

Prompt-based test-time real image dehazing: a novel pipeline

This paper provides a comprehensive survey of methods dealing with visibility
enhancement of images taken in hazy or foggy scenes. The survey begins with
discussing the optical models of atmospheric scattering media and image
formation. This is followed by a survey of existing methods, which are grouped
to multiple image methods, polarizing filters based methods, methods with known
depth, and single-image methods. We also provide a benchmark of a number of
well known single-image methods, based on a recent dataset provided by Fattal
and our newly generated scattering media dataset that contains ground truth
images for quantitative evaluation. To our knowledge, this is the first
benchmark using numerical metrics to evaluate dehazing techniques. This
benchmark allows us to objectively compare the results of existing methods and
to better identify the strengths and limitations of each method.

本文对处理模糊图像的能见度增强方法进行了全面的调查，提供了一个基于定量评估的排名以比较现有方法的结果，并为各种方法的优缺点提供了更好的认识。