In image classification, the label quality of training data critically influences model generalization, especially for deep neural networks (DNNs). Traditionally, learning from noisy labels (LNL) can improve the generalization of DNNs through complex architectures or a series of robust techniques, but its performance improvement is limited by the discriminative paradigm. Unlike traditional ways, we resolve the LNL problems from the perspective of robust label generation, based on diffusion models within the generative paradigm. To expand the diffusion model into a robust classifier that explicitly accommodates more noise knowledge, we propose a Directional Label Diffusion (DLD) model. It disentangles the diffusion process into two paths, i.e., directional diffusion and random diffusion. Specifically, directional diffusion simulates the corruption of true labels into a directed noise distribution, prioritizing the removal of likely noise, whereas random diffusion introduces inherent randomness to support label recovery. This architecture enable DLD to gradually infer labels from an initial random state, interpretably diverging from the specified noise distribution. To adapt the model to diverse noisy environments, we design a low-cost label pre-correction method that automatically supplies more accurate label information to the diffusion model, without requiring manual intervention or additional iterations. Our approach outperforms state-of-the-art methods on both simulated and real-world noisy datasets. Code is available at https://github.com/SenyuHou/DLD.

Numerous significant progress on fisheye image rectification has been achieved through CNN. Nevertheless, constrained by a fixed receptive field, the global distribution and the local symmetry of the distortion have not been fully exploited. To leverage these two characteristics, we introduce FishFormer that processes the fisheye image as a sequence to enhance global and local perception. We tune the Transformer according to the structural properties of fisheye images. First, the uneven distortion distribution in patches generated by the existing square slicing method hinders the understanding of the global structure. Therefore, we propose an annulus slicing method to maintain the consistency of the distortion in each patch; thus, the applicability of the Transformer is expanded to perceive the distortion distribution efficiently. Second, the distortion of adjacent patches is progressive. Such explicit correlations in local regions need to be rapidly constructed and maintained, but Transformer has a weakness in local area perception. Hence, a novel layer attention mechanism is introduced to enhance the local perception and feature interaction. Our network simultaneously implements global perception and focused local perception. Extensive experiments demonstrate that our method provides superior performance compared with state-of-the-art methods.

Although the distortion correction of fisheye images has been extensively studied, the correction of fisheye videos is still an elusive challenge. For different frames of the fisheye video, the existing image correction methods ignore the correlation of sequences, resulting in temporal jitter in the corrected video. To solve this problem, we propose a temporal weighting scheme to get a plausible global optical flow, which mitigates the jitter effect by progressively reducing the weight of frames. Subsequently, we observe that the inter-frame optical flow of the video is facilitated to perceive the local spatial deformation of the fisheye video. Therefore, we derive the spatial deformation through the flows of fisheye and distorted-free videos, thereby enhancing the local accuracy of the predicted result. However, the independent correction for each frame disrupts the temporal correlation. Due to the property of fisheye video, a distorted moving object may be able to find its distorted-free pattern at another moment. To this end, a temporal deformation aggregator is designed to reconstruct the deformation correlation between frames and provide a reliable global feature. Our method achieves an end-to-end correction and demonstrates superiority in correction quality and stability compared with the SOTA correction methods.

Fisheye image rectification is hindered by synthetic models producing poor results for real-world correction. To address this, we propose a Dual Diffusion Architecture (DDA) for fisheye rectification that offers better practicality. The DDA leverages Denoising Diffusion Probabilistic Models (DDPMs) to gradually introduce bidirectional noise, allowing the synthesized and real images to develop into a consistent noise distribution. As a result, our network can perceive the distribution of unlabelled real fisheye images without relying on a transfer network, thus improving the performance of real fisheye correction. Additionally, we design an unsupervised one-pass network that generates a plausible new condition to strengthen guidance and address the non-negligible indeterminacy between the prior condition and the target. It can significantly affect the rectification task, especially in cases where radial distortion causes significant artifacts. This network can be regarded as an alternate scheme for fast producing reliable results without iterative inference. Compared to the state-of-the-art methods, our approach achieves superior performance in both synthetic and real fisheye image corrections.

Fisheye cameras can capture a large field-of-view (Fov) scene but it introduces severe radial distortion in images. Thus, distortion rectification is a crucial step for subsequent computer vision tasks using fisheye cameras. A prevalent type of method predicts the displacement field between the input and output to rectify the distorted images. However, it is challenging to estimate the accurate flow in Cartesian coordinates (both$x$and$y$directions), in which the sampling strategy of the convolution kernel ignores the radial symmetry of distortion. In general, the pixel’s distortion at the same radius from the center is the same, while the radius corresponds to one parameter in polar coordinates. Motivated by this fact, we exploit the radial symmetry of distortion to predict a more straightforward one-dimensional flow, transforming the distorted image into the polar coordinates domain instead of predicting two-dimensional flow in$x$and$y$directions. Specifically, we propose a Polar coordinates Distortion Rectification Network (PCDRN), whose sampling strategy corresponds to the radial distortion characteristic so that a more accurate flow can be predicted. To eliminate the blurs and ring artifacts induced by the coordinates transformation, a Polar-To-Cartesian Appearance Enhancement Network is designed to enhance the local appearance of rectified images. Experimental results on the synthesized dataset and real-world dataset demonstrate the superiority of our approach in both quantitative and qualitative evaluations.

Distortion rectification is often required for fisheye images. The generation-based method is one mainstream solution due to its label-free property, but its naive skip-connection and overburdened decoder will cause blur and incomplete correction. First, the skip-connection directly transfers the image features, which may introduce distortion and cause incomplete correction. Second, the decoder is overburdened during simultaneously reconstructing the content and structure of the image, resulting in vague performance. To solve these two problems, in this paper, we focus on the interpretable correction mechanism of the distortion rectification network and propose a feature-level correction scheme. We embed a correction layer in skip-connection and leverage the appearance flows in different layers to pre-correct the image features. Consequently, the decoder can easily reconstruct a plausible result with the remaining distortion-less information. In addition, we propose a parallel complementary structure. It effectively reduces the burden of the decoder by separating content reconstruction and structure correction. Subjective and objective experiment results on different datasets demonstrate the superiority of our method.

The wide-angle lens gains increasing attention since it can capture a wide field-of-view (FoV) scene. However, the obtained image is contaminated with radial distortion, making the scene not realistic. Previous distortion rectification methods rectify the image in a rectangle or invagination, failing to display the complete content and regular shape simultaneously. In this paper, we rethink the representation of rectification results and present a Rectification OutPainting (ROP) method, aiming to extrapolate the coherent semantics to the blank area and create a wider FoV beyond the original wide-angle lens. To address the specific challenges such as the variable painting region and curve boundary, a rectification module is designed to rectify the image with geometry supervision, and the extrapolated results are generated using a dual conditional expansion strategy. In terms of the spatially discounted correlation, a curve-aware correlation measurement is proposed to focus on the generated region to enforce the local consistency. To our knowledge, we are the first to tackle the challenging rectification via outpainting, and our curve-aware strategy can reach a rectification construction with complete content and regular shape. Extensive experiments well demonstrate the superiority of our ROP over other state-of-the-art solutions.