Section: New Results

Rendering, inpainting and super-resolution

image-based rendering, inpainting, view synthesis, super-resolution

Joint color and gradient transfer through Multivariate Generalized Gaussian Distribution

Participants : Hristina Hristova, Olivier Le Meur.

Multivariate generalized Gaussian distributions (MGGDs) have aroused a great interest in the image processing community thanks to their ability to describe accurately various image features, such as image gradient fields, wavelet coefficients, etc. However, so far their applicability has been limited by the lack of a transformation between two of these parametric distributions. In collaboration with FRVSense (Rémi Cozot and Kadi Bouatouch), we have proposed a novel transformation between MGGDs, consisting of an optimal transportation of the second-order statistics and a stochastic-based shape parameter transformation. We employ the proposed transformation in both color and gradient transfers between images. We have also proposed a new simultaneous transfer of color and gradient.

High-Dynamic-Range Image Recovery from Flash and Non-Flash Image Pairs

Participants : Hristina Hristova, Olivier Le Meur.

In 2016, in collaboration with FRVSense (Rémi Cozot and Kadi Bouatouch), we have proposed a novel method for creating High Dynamic Range (HDR) images from only two images - flash and non-flash images. The proposed method consists of two main steps, namely brightness gamma correction and bi-local chromatic adaptation transform (CAT). First, the brightness gamma correction performs series of increases and decreases of the brightness of the non-flash image and that way yields multiple images with various exposure values. Second, a proposed CAT method, called bi-local CAT enhances the quality of the computed images, by recovering details in the under-/over-exposed regions, using detail information from the flash image. The final multiple exposure images are then merged together to compute an HDR image. Evaluation shows that our HDR images, obtained by using only two LDR images, are close to HDR images, obtained by combining five manually taken multi-exposure images. The proposed method does not require the usage of a tripod and it is suitable for images of non-still objects, such as people, candle flames, etc. Figure 2 illustrates some results of the proposed method. The HDR-VDP-2 color-coded map (right-most image) shows the main luminance differences (the red areas) between our HDR result and the real HDR image. Snippets (a) and (b) show that the proposed method sharpens fine details, e.g. the net on the lamp. The net on the lamp of the real HDR image is blurry, due to a movement in the real multi-exposure images.

Figure 2. HDR image recovery from two input images, i.e. flash and non-flash images. Our HDR result and the real HDR image are tone-mapped for visualization on an LDR display.

Depth inpainting

Participant : Olivier Le Meur.

To tackle the disocclusion inpainting of RGB-D images appearing when synthesizing new views of a scene by changing its viewpoint, in collaboration with Pierre Buyssens from the Greyc laboratory from the Caen University, we have developed a new examplar-based inpainting method of depth map. The proposed method is based on two main components. First, a novel algorithm to perform the depth-map disocclusion inpainting has been proposed. In particular, this intuitive approach is able to recover the lost structures of the objects and to inpaint the depth-map in a geometrically plausible manner. Then, a depth-guided patch-based inpainting method has been defined in order to fill-in the color image. Depth information coming from the reconstructed depth-map is added to each key step of the classical patch-based algorithm from Criminisi et al. in an intuitive manner. Relevant comparisons to state-of-the-art inpainting methods for the disocclusion inpainting of both depth and color images have illustrated the effectiveness of the proposed algorithms.

Super-resolution and inpainting for face recognition

Participants : Reuben Farrugia, Christine Guillemot.

Most face super-resolution methods assume that low- and high-resolution manifolds have similar local geometrical structure, hence learn local models on the low-resolution manifold (e.g. sparse or locally linear embedding models), which are then applied on the high-resolution manifold. However, the low- resolution manifold is distorted by the one-to-many relationship between low- and high- resolution patches.

We have developed a method which learns linear models based on the local geometrical structure on the high-resolution manifold rather than on the low-resolution manifold. For this, in a first step, the low-resolution patch is used to derive a globally optimal estimate of the high-resolution patch. The approximated solution is shown to be close in Euclidean space to the ground-truth but is generally smooth and lacks the texture details needed by state-of-the-art face recognizers. Unlike existing methods, the sparse support that best estimates the first approximated solution is found on the high-resolution manifold. The derived support is then used to extract the atoms from the coupled dictionaries that are most suitable to learn an upscaling function between the low- and high- resolution patches.

The proposed solution has also been extended to compute face super-resolution of non-frontal images. Experimental results show that the proposed method out- performs six face super-resolution and a state-of-the-art cross- resolution face recognition method. These results also reveal that the recognition and quality are significantly affected by the method used for stitching all super-resolved patches together, where quilting was found to better preserve the texture details which helps to achieve higher recognition rates. The proposed method was shown to be able to super-resolve facial images from the IARPA Janus Benchmark A (IJB-A) dataset which considers a wide range of poses and orientations.

A method has also been developed to inpaint occluded facial regions with unconstrained pose and orientation. This approach first warps the facial region onto a reference model to synthesize a frontal view [15]. A modified Robust Principal Component Analysis (RPCA) approach is then used to suppress warping errors. It then uses a novel local patch-based face inpainting algorithm which hallucinates missing pixels using a dictionary of face images which are pre-aligned to the same reference model. The hallucinated region is then warped back onto the original image to restore missing pixels. Experimental results on synthetic occlusions demonstrate that the proposed face inpainting method has the best performance achieving PSNR gains of up to 0.74dB over the second-best method. Moreover, experiments on the COFW dataset and a number of real-world images show that the proposed method successfully restores occluded facial regions in the wild even for Closed-Circuit Television (CCTV) quality images.

Light-field inpainting

Participants : Christine Guillemot, Xiaoran Jiang, Mikael Le Pendu.

Building up on the advances in low rank matrix completion, we have developed a novel method for propagating the inpainting of the central view of a light field to all the other views. After generating a set of warped versions of the inpainted central view with random homographies, both the original light field views and the warped ones are vectorized and concatenated into a matrix. Because of the redundancy between the views, the matrix satisfies a low rank assumption enabling us to fill the region to inpaint with low rank matrix completion. To this end, a new matrix completion algorithm, better suited to the inpainting application than existing methods, has also been developed. Unlike most of the existing light field inpainting algorithms, our method does not require any depth prior. Another interesting feature of the low rank approach is its ability to cope with color and illumination variation between the input views of the light field (see Fig.3. As it can be seen in Figure 3, the proposed method yields inpainting consistency across views.

Figure 3. Illustration of our inpainting propagation method : (a) Original central view. (b) Inpainted central view. (c) Another view of the light field inpainted with a state-of-the-art 2D image inpainting method. (d) Propagated inpainting from central view to a different view with the developed low rank method.