Implicit Neural Image Stitching (2309.01409v5)

Abstract: Existing frameworks for image stitching often provide visually reasonable stitchings. However, they suffer from blurry artifacts and disparities in illumination, depth level, etc. Although the recent learning-based stitchings relax such disparities, the required methods impose sacrifice of image qualities failing to capture high-frequency details for stitched images. To address the problem, we propose a novel approach, implicit Neural Image Stitching (NIS) that extends arbitrary-scale super-resolution. Our method estimates Fourier coefficients of images for quality-enhancing warps. Then, the suggested model blends color mismatches and misalignment in the latent space and decodes the features into RGB values of stitched images. Our experiments show that our approach achieves improvement in resolving the low-definition imaging of the previous deep image stitching with favorable accelerated image-enhancing methods. Our source code is available at https://github.com/minshu-kim/NIS.

Implicit Neural Image Stitching with Enhanced and Blended Feature Reconstruction

The paper "Implicit Neural Image Stitching with Enhanced and Blended Feature Reconstruction" introduces an innovative framework for image stitching, addressing the limitations of existing approaches that often suffer from blurry artifacts and inconsistencies in illumination and depth. This new approach, termed Neural Image Stitching (NIS), stands out by incorporating implicit neural representation techniques which have demonstrated success in high-frequency detail recovery, particularly in super-resolution tasks. The primary objective is to enhance image quality in panoramic views while rectifying common stitching artifacts.

Methodology Overview

The NIS framework is grounded in the principles of implicit neural representation (INR), which captures continuous signals through neural networks, an approach that has recently seen successful applications in arbitrary-scale super-resolution. The authors propose an intriguing method whereby NIS uses Fourier coefficients to predict high-quality warped images. This is particularly relevant as it extends the concept of arbitrary-scale super-resolution to image stitching, allowing for improved image reconstruction.

The architecture of NIS consists of three main components: neural warping, a blender, and a decoder. The neural warping module is responsible for extracting high-frequency-aware features from the input images, which consist of a reference and a target image. These features are then aligned via pre-trained transformation estimators. The blended features are processed within a latent space, which is crucial for correcting color mismatches and minimizing parallax errors. The decoder, implemented as a multilayer perceptron (MLP), outputs the final RGB image by mapping processed features to pixel values in the image domain.

Key Technical Contributions

1. Implicit Neural Representation for Stitching: The authors integrate INR to achieve high-frequency detail recovery in the stitched images, addressing the spectral bias limitation inherent in standard neural networks.
2. Fourier Coefficient Estimation: By predicting Fourier coefficients, the model ensures high-quality reconstruction and effective image warping, which is instrumental in maintaining texture consistency across stitched frames.
3. Simplified Pipeline: The proposed method unifies several processes, including warping, blending, and image enhancement, into a single, streamlined inference pipeline, potentially improving both efficiency and performance.

Experimental Results

The experiments conducted demonstrate that the NIS framework significantly outperforms existing methods in both synthetic and real-world scenarios, as evaluated by common metrics such as PSNR and SSIM on synthetic datasets, and NIQE, PIQE, and BRISQUE on real datasets. Particularly notable is the improvement in resolving low-definition artifacts, where NIS achieves a considerable boost in PSNR over traditional interpolation methods like bicubic and bilinear. Additionally, the visual quality of stitches on real-world datasets shows enhanced detail and reduced artifact presence compared to prior art.

Implications and Future Directions

The authors suggest that NIS's ability to maintain high-frequency detail can lead to more accurate and visually pleasing panoramic images, making it a promising tool for applications requiring high-quality panoramic views, such as virtual reality, medical imaging, and autonomous driving. Furthermore, the efficient blending of misaligned features has potential applications in enhanced remote sensing and surveillance imaging.

Moving forward, there are opportunities to extend the NIS approach beyond static frame stitching to dynamically captured scenes, potentially incorporating real-time adjustments and on-the-fly rendering for continuous and immersive viewing experiences. Moreover, adapting the framework to handle multiple input modalities could further improve its applicability across diverse domains.

In conclusion, this paper presents a substantial advance in the field of image stitching, leveraging recent developments in neural representation to offer a comprehensive solution to longstanding challenges in the field. The integration of Fourier-based feature prediction with implicit neural reconstruction paves the way for further exploration and deployment of neural methods in complex image processing tasks.